In this study, the effects environmental variables on malaria incidence were measured by OLS, GWR and s-GWR models for each year, 2015 and 2016, across 679 wards in Ethiopia. In the study area, the high-risk region for malaria, and spatial clustering appeared in the distribution of malaria incidence for both years. All three models considered the same set of explanatory variables, which were temperature, elevation, relative humidity, precipitation, and a predictor variable derived from remote sensing data (NDVI).
The results of this study showed that malaria incidence in Ethiopia during the study period heterogeneously distributed and spatially clustered at the ward level in the country. The results are consistent with research findings from past studies conducted in various malaria-endemic regions of the world (Delmelle et al., 2016; Wijayanti et al., 2016; Acharya et al., 2018; Lin and Wen, 2011).
This research is the first ward-level malaria study using the s-GWR model in entire Ethiopia, which explained the modeling malaria incidence associated with environmental risk factors in the country. The finding could be helpful for ward-level planning, policy formulation, and implementation of malaria control.
Our study showed the importance of the semiparametric geographical modeling approach of local-level risk factors analysis by contrasting global (OLS), local (GWR), and mixed (s-GWR) model. Our analysis exhibited the drawbacks of the OLS method to explain spatial variation of malaria incidence in terms of predictive performance and model accuracy and complexities evaluated to the GWR model. We showed that model goodness-of-fit could be improved through the implementation of
the s-GWR model. These findings are concurrent with malaria study in Ghana (Ehlkes et al., 2014), and dengue fever in Jhapa district, Nepal (Acharya et al., 2018). However, when independent variables do not show spatial non-stationarity, the ordinary least squares regression model is generally recommended to avoid the model complexity instead of GWR or s-GWR (Ramezankhani et al., 2017).
As a rule of thumb, a “serious” difference between GWR and OLS models generally regarded as one where the dissimilarity in AICc values between the models is at least 3 (Fotheringham, Charlton and Brunsdon, 1998). The s-GWR models had the smallest AICc values for 2015 and 2016, so it was the best model.
The Global Moran’s I of the residuals of the final s-GWR model in 2015 was − 0.059589 (z score = -2.653625 and p-value = 0.012), which indicates that in 2015 there was significant spatial autocorrelation in the residuals of the model, thus it was not correctly specified (i.e. key explanatory variables are likely to be missing). In 2016, the Moran’s I of the residuals of the final s-GWR model was − 0.079349 (z score = -3.622420 and p-value = 0.053), so there is no significant spatial autocorrelation in the residual and the model was properly specified.
A significant benefit of the s-GWR model is the ability to visually represent the varying strength of association between the response and explanatory variables (Buck, 2016). Moreover, it facilitates the interpretation based on spatial context and known characteristics of the study area (Goodchild and Janelle, 2004). The variation in local R2 over the wards revealed significant regional differences in the malaria incidence transmission process in the study area (Fig. 7a-b).The local R2 depicted that the local model had higher performance in hotspots areas when it compared to the other parts of the study area matching with previous parallel studies from Nepal (Acharya et al., 2018), Colombia(Delmelle et al., 2016) and South Africa (Manyangadze et al., 2016).
Figure 7.Local R2 (a, b), residual distribution (c, d) in the s-GWR-based prediction model of the malaria case in 2015 and 2016
Our final mixed s-GWR models show that the distribution of annual malaria incidence is heterogeneous (Fig. 5 and Fig. 6) as observed in other studies (Pinchoff et al., 2015;Rulisa et al., 2013; Parker et al., 2015).
According to researchers in Ethiopia, Brazil and Cambodia (Alemu et al., 2011; De Castro et al., 2006; Dysoley et al., 2008; Hasyim et al., 2018) the environmental risk factors were significantly correlation with malaria incidence that vary strongly at the village level. Identifying the malaria incidence clusters (areas with a high number of malaria incidences) is critical in developing or improving malaria planning and control strategies at the ward scale in the country. Dissimilarities of malaria pattern exist between different regions (Guthmann et al., 2002). Thus in our study also indicated that the pattern of malaria incidence distribution is not the same in the study area; it changes from year to year in the country. The malaria incidence clusters may point to the wards that require prompt notice in terms of planning and implementation of the disease control strategies.
The heterogeneity of malaria incidence determined by a variety of ecological, biological, and sociological factors (Pinchoff et al., 2015). As noted by researcher (Ehlkes et al., 2014), nearly all studies assume homogeneous influence of independent variables (Mushinzimana et al., 2006; Dambach et al., 2012; Stefani et al., 2011) but this may not always be most appropriate (Nakaya et al., 2005). In our study, the analysis result showed that assuming that some variables vary at the local level, while others have a global effect, substantially improves the model performance. Permitting spatial heterogeneity within the regression model allows clear interpretation regarding the true nature of the potential relationship (Ehlkes et al., 2014). That could be due to the Long-lasting insecticidal nets (LLINs) distributed to some of the wards that have malaria incidence in the country. Long-lasting insecticidal nets (LLINs) are a tool to control malaria vector in malaria epidemic areas effectively (Masaninga et al., 2018). When assessing the relationship between environmental variables and malaria incidence, one should think about the pathways in which these variables under study lie (Ehlkes et al., 2014). For instance, the environmental variables: temperature, NDVI, elevation, relative humidity, and precipitation, which influence the malaria inciencde considered in this study as they determine the plenty of mosquito or their breeding habitats.
In Ethiopia, malaria control strategies include indoor residual spraying (IRS) and LLINs are applied based on the local setting (Loha et al., 2019). Those factors tend to reduce the incidence of malaria. The interaction between these factors and malaria incidence may bring out unexpected results, defying the norms regarding the association between environmental risk factors and malaria disease.
In this research, there was an association between elevation and malaria incidence. Internationally, Anopheline species diversity and density decrease from the lowlands to highlands (Hasyim et al., 2018). Therefore, poor inhabitants living in forested lowland areas in Papua, Indonesia, were found to be at a higher risk of malaria disease than those in the highlands (Hanandita and Tampubolon, 2016).
In contrast, a positive association between elevation and plenty of Anopheles mosquitoes has noticed in the highlands of Ethiopia, Colombia, and Ecuador, mainly in warmer years (Siraj et al., 2014; Pinault and Hunter, 2011; Alimi et al., 2015). It has been accepted that malaria transmission possible decreases as the altitude increases (Chikodzi, 2013; Meyrowitsch et al., 2011). In our study, also we noted elevation was significant in 2015 and showed its expected negative relationship with malaria incidence in some of the wards in the northern and southern wards, but also showed a positive correlation in some wards to the western part of the country (Fig. 5a).
In Ethiopia, precipitation was significantly correlated with malaria incidence in tropical areas (Midekisa et al., 2015). Moreover, in Botswana, precipitation showed association with the incidence of clinical malaria incidence (Chirebvu et al., 2016). Variations in monthly rainfall in rural Tanzania primarily correlated with malaria incidence (Thomson et al., 2017). In South Africa, the number of malaria incidence was significantly positively associated with higher winter precipitation (Kleinschmidt et al., 2001). In this study, coefficients of precipitation in 2015 showed the expected positive and negative relationship with malaria incidence in some wards in the country. Precipitation was significant in some rural wards located in the northwestern, western, central, and southwestern part of the country as depicted in Fig. 5b. In Ethiopia, minimum temperatures significantly correlated with malaria incidence in cold areas (Midekisa et al., 2015). In this study also local coefficients of temperature in 2015 showed positive and negative relationship with malaria incidence and were significant in some wards located in the northwestern and western part of the country, as depicted in Fig. 5c.
Precipitation creates oviposition sites for female mosquitoes, whereas relative humidity is a crucial parameter for adult mosquito daily survival (Day, 2016). Anopheline mosquitoes need stagnant water to wind up their larval and pupal development. Thus, precipitation and relative humidity affect the transmission of malaria by providing water to create aquatic habitats. In this study also local coefficients of relative humidity in 2015 showed the expected positive and negative relationship with malaria incidence, and they were significant in some wards located in the northwestern and western part of the country, as depicted in Fig. 5d. Anopheles (Cellia) leucosphyrus is the type of malaria that can be transmitted in forested areas of Sumatra (Elyazar et al., 2013). In 2016, NDVI local coefficients showed an only positive relationship with malaria incidence in some wards in the country. NDVI was significant in some wards located in the northern part of the country, as it showed in Fig. 6a. In 2016 precipitation local coefficients showed an only negative relationship with malaria incidence and were significant in some wards located in the northern part of the country, as it showed in Fig. 6b. In 2016 relative humidity local coefficients showed a positive and negative relationship with malaria incidence in some wards in the country. Relative humidity was significant in some wards located in the northern part of the country (Fig. 6c). This indicated that s-GWR successfully captured the spatial stationary and non-stationary to model the factors that influence the spread of malaria incidence.
The weak positive and weak negative relationships between environmental risk factors and malaria occurrences in some of the wards could be due to the protective effect of malaria control factors, for example, vector control methods including LLINs and IRS. These malaria interferences contribute significantly to the decline in malaria incidence, mainly in areas progressing towards malaria elimination (Meyrowitsch et al., 2011). Researchers (Gwitira et al., 2015) also noted that in malaria incidence where there is effective malaria control, there would be weak correlations among habitat suitability and malaria incidence. This was observed in this study in 2015, NDVI was a weak association with malaria incidence in the country. In 2016 elevation and temperature were also weak correlations with malaria incidence in the country.
Temperature, precipitation, and relative humidity are frequently used to predict for the spatial, seasonal, and interannual variation for malaria transmission, such as the dynamic malaria model forecasting malaria occurrence with seasonal climate (Hoshen and Morse, 2004). Land use, relative humidity, elevation, and precipitation have been identified by GWR to determine the regional vulnerability to malaria incidence in Purworejo, Indonesia (Hasyim et al., 2018). The GWR model revealed here in our study that elevation, temperature, precipitation, relative humidity, and NDVI significantly influence malaria incidence in some wards in Ethiopia. Similarly, in 2015 elevation, temperature, precipitation, and relative humidity have been identified by s-GWR and were significantly influence malaria incidence in some of the wards in Ethiopia. Similarly, in 2016 precipitation, NDVI, and relative humidity have been identified by the s-GWR model and were significantly influence malaria incidence in some wards in Ethiopia. However, s-GWR model allowing for spatial heterogeneity explains better the relationship of malaria incidence with environmental risk factors in Ethiopia. Similarly, in Venezuela, the GWR model analysis showed that ecological relations that act on different scales play a role in malaria transference and that modeling increases the understanding of important spatiotemporal inconsistency (Hasyim et al., 2018).